code: def _concat(obj):
      first_item = obj[0]
      if isinstance(first_item, (list, tuple)):
          return type(first_item)(_concat(ele) for ele in zip(*obj))
      if isinstance(first_item, dict):
          concat_dict = {}
          for key in first_item.keys():
              concat_dict[key] = _concat([o[key] for o in obj])
          return concat_dict
      if (
          isinstance(first_item, np.ndarray)
          and len(first_item.shape) != 0
      ):
          return np.concatenate(obj)
      if torch.is_tensor(first_item) and len(first_item.shape) != 0:
          return torch.cat(obj)
      return obj
  
imports: import contextlib
  import numbers
  import os
  import random
  import warnings
  from typing import IO, Any, BinaryIO, Optional, Union
  import numpy
  import torch
  from torch.nn.utils.rnn import PackedSequence
  from torch.utils.data import Dataset
  from poutyne.warning_manager import warning_settings
label_desc: Use image node_id 5 for calling a global function with example usage: _concat(obj) and returns: obj, type, concat_dict, np, torch